Considering the implications of climate-induced species range shifts in marine protected areas planning
Bibliographic record
Abstract
Climate change is affecting the ocean, altering the biogeography of marine species. Yet marine protected area (MPA) planning still rarely incorporates projected species range shifts. We used the outputs of species distribution models fitted with biological and climate data as inputs to identify trends in occurrence for marine species in British Columbia (BC), Canada. We assessed and compared two ways of incorporating climate change projections into MPA planning. First, we overlaid 98 species with modelled distributions now and by the mid-21st century under two contrasting (“no mitigation” and “strong mitigation”) climate change scenarios with existing Provincial marine parks in BC, to ask which species could overlap with protected areas in the future. Second, we completed a spatial prioritization analysis using Marxan with the projected future species ranges as inputs, to ask where priority regions exist for the 98 marine species. We found that many BC marine parks will lose species in both climate scenarios that we analyzed, and that protecting 30% of important marine species will be challenging under the “no mitigation” climate change scenario. Challenges included the coarse resolution of the data and uncertainty in projecting species range shifts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".